Learn how to use regression models, the most important statistical analysis tool in the data scientist's toolkit. This is the seventh course in the Johns Hopkins Data Science Specialization.
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
In this course students will learn how to fit regression models, how to interpret coefficients, how to investigate residuals and variability. Students will further learn special cases of regression models including use of dummy variables and multivariable adjustment. Extensions to generalized linear models, especially considering Poisson and logistic regression will be reviewed.
R programming, mathematical aptitude. The content in the R Programming and Statistical Inference courses covers the necessary background. The material from Statistical inference could be taken concurrently with this class.
Weekly lecture videos and quizzes and a final peer-assessed project.
Will I get a Statement of Accomplishment after completing this class?
Free statements of accomplishment are not offered in this course. If you are not enrolled in Signature Track, participation and performance documentation will be reported on your Accomplishments page, but you will not receive a signed statement of accomplishment.
What resources will I need for this class?
Students must have the latest version of R and RStudio installed.
How does this course fit into the Data Science Specialization?
This is the seventh course in the sequence. Although it isn't a requirement, we recommend that you first take The Data Scientist's Toolbox and R Programming.